SmartSharing: A CDN with Smart Contract-based Local OTT Sharing
Bibliographic record
Abstract
A content delivery network (CDN) uses distributed cache servers to reduce the content delivery latency to end users. In recent years, CDN providers adopt a new content caching strategy that allows end users to share their storage/bandwidth resources. Two core questions need to answer in this strategy: (1) how to incentivize end users to contribute their resources? (2) how to facilitate transparent, secure content trading among end users?We propose a new CDN solution, called SmartSharing, where users contribute their over-the-top (OTT) devices as mini-cache servers. To incentivize end users to contribute resources, SmartSharing uses game theory and an Expectation-Maximization (EM) algorithm to determine the content delivery schedule and the pricing scheme. To facilitate content trading among end users, SmartSharing uses smart contracts in Ethereum to create a transparent and safe transaction platform. We thoroughly evaluate the performance of SmartSharing with real-world trace-driven simulation as well as a prototype using content metadata and the derived pricing scheme.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".